National and Subnational estimates for Russia

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in Russia. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively (see Methods for further explanation).

Table of Contents


Using data available up to the: 2020-05-28

Note that it takes time for infection to cause symptoms, to get tested for SARS-CoV-2 infection, for a positive test to return and ultimately to enter the case data presented here. In other words, today’s case data are only informative of new infections about two weeks ago. This is reflected in the plots below, which are by date of infection.

Expected daily confirmed cases by region


Figure 1: The results of the latest reproduction number estimates (based on estimated confirmed cases with a date of infection on the 2020-05-14) in Russia, stratified by region, can be summarised by whether confirmed cases are likely increasing or decreasing. This represents the strength of the evidence that the reproduction number in each region is greater than or less than 1, respectively (see the methods for details). Regions with fewer than 40 confirmed cases reported on a single day are not included in the analysis (light grey).

National summary

Summary (estimates as of the 2020-05-14)

Table 1: Latest estimates (as of the 2020-05-14) of the number of confirmed cases by date of infection, the expected change in daily confirmed cases, the effective reproduction number, the doubling time (when negative this corresponds to the halving time), and the adjusted R-squared of the exponential fit. The mean and 90% credible interval is shown for each numeric estimate.
Estimate
New confirmed cases by infection date 9612 (8963 – 10219)
Expected change in daily cases Likely increasing
Effective reproduction no. 1 (1 – 1.1)
Doubling/halving time (days) 120 (54 – -500)
Adjusted R-squared 0.46 (1.8e-05 – 0.79)

Confirmed cases, their estimated date of infection, and time-varying reproduction number estimates


Figure 2: A.) Confirmed cases by date of report (bars) and their estimated date of infection. B.) Time-varying estimate of the effective reproduction number. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Estimates from existing data are shown up to the 2020-05-14 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.

Time-varying rate of growth and doubling time


Figure 3: A.) Time-varying estimate of the rate of growth, B.) Time-varying estimate of the doubling time in days (when negative this corresponds to the halving time), C.) The adjusted R-squared estimates indicating the goodness of fit of the exponential regression model (with values closer to 1 indicating a better fit). Estimates from existing data are shown up to the 2020-05-14. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.

Regional Breakdown

Data availability

Limitations

Summary of latest reproduction number and confirmed case count estimates by date of infection


Figure 4: Confirmed cases with date of infection on the 2020-05-14 and the time-varying estimate of the effective reproduction number (light bar = 90% credible interval; dark bar = the 50% credible interval.). Regions are ordered by the number of expected daily confirmed cases and shaded based on the expected change in daily confirmedcases. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination.

Reproduction numbers over time in the six regions expected to have the most new confirmed cases


Figure 5: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-05-14 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control. The vertical dashed line indicates the date of report generation.

Confirmed cases and their estimated date of infection in the six regions expected to have the most new confirmed cases


Figure 6: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-05-14 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.

Reproduction numbers over time in all regions


Figure 7: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates from existing data are shown up to the 2020-05-14 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control. The vertical dashed line indicates the date of report generation.

Confirmed cases and their estimated date of infection in all regions

Figure 8: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates from existing data are shown up to the 2020-05-14 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.

Latest estimates (as of the 2020-05-14)

Table 2: Latest estimates (as of the 2020-05-14) of the number of confirmed cases by date of infection, the effective reproduction number, and the doubling time (when negative this corresponds to the halving time) in each region. The mean and 90% credible interval is shown.
Region New confirmed cases by infection date Expected change in daily cases Effective reproduction no. Doubling/halving time (days)
Altayskiy kray 54 (39 – 68) Likely increasing 1.2 (0.9 – 1.4) 17 (7 – -37)
Arkhangelsk oblast 45 (28 – 59) Unsure 1.1 (0.8 – 1.3) 38 (8.5 – -15)
Astrahan oblast 49 (31 – 63) Unsure 1.1 (0.8 – 1.3) 61 (9.1 – -13)
Belgorod oblast 75 (56 – 91) Unsure 1.1 (0.9 – 1.2) 100 (13 – -17)
Briansk oblast 110 (87 – 129) Likely increasing 1.1 (1 – 1.3) 25 (10 – -60)
Chechen republic 25 (14 – 37) Unsure 1 (0.7 – 1.3) -170 (8.5 – -8)
Cheliabinsk oblast 85 (65 – 103) Unsure 1.1 (0.9 – 1.2) 58 (12 – -21)
Habarovskiy kray 60 (41 – 75) Unsure 1.1 (0.9 – 1.3) 29 (8.6 – -22)
Hanty-Mansiyskiy AO 73 (55 – 89) Unsure 1.1 (0.9 – 1.3) 53 (11 – -19)
Ingushetia republic 46 (31 – 60) Unsure 1 (0.8 – 1.3) 51 (9.2 – -14)
Irkutsk oblast 63 (46 – 79) Likely increasing 1.2 (1 – 1.4) 15 (6.8 – -85)
Ivanovo oblast 49 (35 – 65) Unsure 0.9 (0.7 – 1.1) -21 (29 – -7.7)
Kaliningrad oblast 37 (23 – 49) Unsure 1 (0.8 – 1.3) 77 (8.9 – -11)
Kaluga oblast 118 (95 – 139) Likely increasing 1.1 (0.9 – 1.2) 36 (12 – -36)
Kamchatskiy kray 23 (11 – 33) Likely increasing 1.2 (0.8 – 1.6) 15 (4.8 – -14)
Kirov oblast 38 (24 – 50) Likely increasing 1.2 (0.9 – 1.5) 18 (6.2 – -20)
Komi republic 29 (17 – 40) Likely increasing 1.2 (0.9 – 1.5) 13 (4.9 – -21)
Krasnodarskiy kray 94 (72 – 112) Unsure 1 (0.8 – 1.1) -250 (18 – -16)
Krasnoyarskiy kray 241 (208 – 272) Increasing 1.4 (1.2 – 1.6) 7.7 (5.5 – 13)
Kursk oblast 82 (62 – 100) Unsure 1 (0.9 – 1.2) 73 (13 – -20)
Leningradskaya oblast 70 (49 – 87) Unsure 1 (0.8 – 1.2) -1700 (14 – -14)
Lipetsk oblast 66 (46 – 80) Likely increasing 1.1 (0.9 – 1.3) 22 (8.1 – -30)
Magadan oblast 11 (2 – 19) Likely increasing 1.4 (0.7 – 2.1) 7.5 (2.7 – -9.6)
Moscow 3493 (3296 – 3711) Decreasing 0.9 (0.8 – 0.9) -22 (-34 – -16)
Moscow oblast 986 (905 – 1073) Likely increasing 1 (1 – 1.1) 120 (34 – -82)
Murmansk oblast 43 (28 – 57) Unsure 1 (0.8 – 1.3) 46 (8.7 – -14)
Nizhegorodskaya oblast 235 (205 – 267) Likely decreasing 0.9 (0.9 – 1) -36 (120 – -15)
Novosibirsk oblast 77 (56 – 94) Unsure 1 (0.9 – 1.2) 65 (12 – -19)
Omsk oblast 62 (44 – 77) Likely increasing 1.2 (1 – 1.4) 15 (6.7 – -69)
Orel oblast 50 (33 – 65) Unsure 0.9 (0.7 – 1.1) -37 (18 – -9.2)
Orenburg oblast 45 (29 – 60) Unsure 1 (0.8 – 1.2) -68 (13 – -9.4)
Pensa oblast 83 (63 – 99) Increasing 1.2 (1 – 1.4) 14 (6.9 – 250)
Perm oblast 54 (36 – 68) Likely increasing 1.1 (0.9 – 1.4) 24 (7.8 – -23)
Primorskiy kray 58 (40 – 74) Unsure 1 (0.8 – 1.2) 7400 (13 – -13)
Republic of Adygeia 44 (29 – 59) Likely increasing 1.3 (0.9 – 1.6) 12 (5.5 – -74)
Republic of Bashkortostan 92 (68 – 109) Unsure 1 (0.9 – 1.2) 57 (12 – -22)
Republic of Buriatia 41 (26 – 55) Unsure 1 (0.8 – 1.2) -71 (13 – -9.4)
Republic of Chuvashia 83 (64 – 101) Unsure 1 (0.9 – 1.2) 120 (14 – -18)
Republic of Dagestan 99 (75 – 117) Unsure 1 (0.8 – 1.1) -710 (17 – -16)
Republic of Hakassia 31 (18 – 43) Unsure 1.1 (0.8 – 1.4) 37 (7.1 – -11)
Republic of Kabardino-Balkaria 70 (50 – 86) Unsure 1 (0.8 – 1.2) -140 (15 – -12)
Republic of Kalmykia 30 (18 – 41) Unsure 1 (0.7 – 1.3) -160 (10 – -8.7)
Republic of Karachaevo-Cherkessia 18 (6 – 27) Unsure 1 (0.6 – 1.4) -250 (6.7 – -6.7)
Republic of Mariy El 46 (30 – 62) Unsure 1.1 (0.8 – 1.3) 49 (9.1 – -15)
Republic of Mordovia 38 (23 – 50) Unsure 1 (0.8 – 1.3) 58 (8.2 – -12)
Republic of North Osetia - Alania 75 (53 – 91) Unsure 1 (0.8 – 1.2) 490 (14 – -15)
Republic of Tatarstan 80 (58 – 97) Unsure 1 (0.8 – 1.1) -1600 (15 – -15)
Republic of Tyva 54 (38 – 68) Increasing 1.3 (1 – 1.6) 11 (5.5 – 570)
Republic of Udmurtia 13 (4 – 21) Unsure 1.2 (0.7 – 1.7) 17 (4 – -7.7)
Rostov oblast 131 (107 – 151) Likely increasing 1.1 (1 – 1.3) 21 (9.7 – -120)
Ryazan oblast 94 (71 – 112) Unsure 1 (0.9 – 1.2) 510 (16 – -17)
Saha republic 54 (38 – 68) Unsure 1.1 (0.9 – 1.3) 35 (9.1 – -19)
Saint Petersburg 479 (429 – 526) Likely increasing 1.1 (1 – 1.1) 44 (20 – -220)
Samara oblast 79 (59 – 97) Unsure 1.1 (0.9 – 1.2) 57 (12 – -19)
Saratov oblast 94 (70 – 111) Unsure 1 (0.8 – 1.2) -240 (17 – -16)
Sevastopol 10 (1 – 20) Likely increasing 1.7 (0.5 – 2.8) 7.7 (2.1 – -4.7)
Smolensk oblast 58 (40 – 74) Unsure 1 (0.8 – 1.2) -200 (13 – -12)
Stavropolskiy kray 68 (47 – 83) Unsure 1.1 (0.9 – 1.2) 51 (11 – -19)
Sverdlov oblast 143 (121 – 170) Unsure 1.1 (0.9 – 1.2) 47 (14 – -34)
Tambov oblast 86 (65 – 104) Unsure 1 (0.9 – 1.2) 600 (15 – -16)
Tomsk oblast 22 (9 – 31) Unsure 1.1 (0.7 – 1.5) 39 (5.7 – -8)
Tula oblast 94 (71 – 111) Unsure 1 (0.9 – 1.2) -950 (16 – -16)
Tumen oblast 41 (25 – 53) Unsure 1 (0.7 – 1.2) -31 (17 – -8)
Tver oblast 36 (23 – 49) Unsure 1.1 (0.8 – 1.3) 38 (7.7 – -13)
Ulianovsk oblast 81 (60 – 100) Unsure 1.1 (0.9 – 1.3) 39 (10 – -23)
Vladimir oblast 75 (55 – 91) Unsure 1 (0.8 – 1.2) 120 (13 – -17)
Volgograd oblast 83 (62 – 100) Unsure 1 (0.8 – 1.1) -130 (17 – -14)
Voronezh oblast 44 (30 – 57) Unsure 1 (0.8 – 1.2) 290 (11 – -12)
Yamalo-Nenetskiy AO 65 (46 – 79) Unsure 1 (0.8 – 1.1) -73 (18 – -12)
Yaroslavl oblast 116 (91 – 135) Likely increasing 1.1 (1 – 1.3) 24 (10 – -70)
Zabaykalskiy kray 36 (23 – 49) Unsure 1.1 (0.8 – 1.4) 40 (7.8 – -12)

Abbott, Sam, Joel Hellewell, James D. Munday, and Sebastian Funk. 2020. “NCoVUtils: Utility Functions for the 2019-Ncov Outbreak.” - - (-): –. https://doi.org/10.5281/zenodo.3635417.

Mironov, Sergey. 2020. “COVID-19 Data from Jhu Csse, Updated with Details on Russian Regions.” Github Repository. https://github.com/grwlf/COVID-19_plus_Russia.

Xu, Bo, Bernardo Gutierrez, Sarah Hill, Samuel Scarpino, Alyssa Loskill, Jessie Wu, Kara Sewalk, et al. n.d. “Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data.” http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337.